Idiopathic pulmonary fibrosis (IPF) is an inflammatory, interstitial disease of unknown origin which is characterized by a gradual deterioration over months to years, although the natural history can vary widely with some patients demonstrating prolonged stability. Many investigators have suggested clinical, demographic or physiologic features which may suggest a better long term prognosis although no consensus has been reached. Recently high resolution computed tomography (HRCT) and histopathologic classification have been advocated as better able to predict those patients likely to respond to immunosuppressive therapy. Limited data are available contrasting these two techniques although the presence of nonspecific interstitial pneumonia (NSIP) appears to be associated with an improved long term prognosis. We propose to define the incidence of NSIP in a large cohort of patients with IPF (n=160) who are fully characterized clinically, physiologically and radiographically, including semiquantitative scoring of HRCT ground glass or fibrotic (CT-fib) opacity. Two chest histopathologists will independently examine open lung biopsies, patient by patient, and by individual patient lobar biopsies in a blinded fashion. This will allow determination of intra- and inter-observer agreement. Clinical, physiologic and radiographic features will be defined for patients with NSIP. The independent ability of histopathologic classification to predict long term survival will be contrasted with HRCT scoring and clinical and physiologic features. It is hypothesized that the degree of CT-fib abnormality will better predict long term outcome than histopathologic classification. We also aim to measure neutrophilic infiltration of the lung using 18FDG positron emission tomography as a modality which will prove additive to HRCT scoring in predicting short term response to therapy and long term survival. Finally, we aim to demonstrate that serial HRCT scoring will improve our ability to define response to immunosuppressive therapy compared to a composite score of clinical, physiologic and radiographic features (CRP). These data will allow optimal identification of patients who will most likely respond to standard therapy and those with little likelihood of response or high risk of side effects. These latter patients will be considered for early institution of novel therapies which will allow the translation of the findings of basic investigators to evidence-based, patient oriented research.